Optimize AI Model Zoo Workloads with PyTorch* for 4th Generation Intel® Xeon® Scalable Processors

ID 772111
Updated 2/10/2023
Version Latest





This document provides links to step-by-step instructions on how to leverage Model Zoo docker containers to run optimized open-source Deep Learning Training and Inference workloads using PyTorch* framework on 4th Generation Intel® Xeon® Scalable processors.

Note: The containers below are based on pre-production build for Intel®  Extenstion for PyTorch* and are for customer preview only and are not intended for use in production. 

Use cases

The tables below provide links to run each use case using docker containers. The model scripts run on Linux.

Image Recognition

Model Documentation Link Dataset   
ResNet 50 Training ImageNet 2012
ResNet 50 Inference ImageNet 2012
ResNext-32x16d Inference ImageNet 2012





Object Detection

Model Documentation Link Dataset   
Mask R-CNN Training COCO 2017
Mask R-CNN Inference COCO 2017 
SSD-ResNet34 Training COCO 2017
SSD-ResNet34 Inference COCO 2017





Language Modeling

Model Documentation Link Dataset   
BERT large Training Preprocessed Text dataset
BERT large Inference SQuAD1.0
RNN-T Inference RNN-T dataset
DistilBERT base Inference DistilBERT Base SQuAD1.1






Model Documentation Link Dataset   
DLRM Training Criteo Terabyte
DLRM Inference Criteo Terabyte




Documentation and Sources 

Get Started                                Code Sources                                              

Main GitHub*                                                         Dockerfiles

Release Notes                                                       Report Issue                                                                         

License Agreement

LEGAL NOTICE: By accessing, downloading or using this software and any required dependent software (the “Software Package”), you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party software included with the Software Package. Please refer to the license file for additional details.

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